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The Network Workbench Community Wiki is the part of Network Workbench (NWB) project. It provides descriptions for algorithms and datasets that have been integrated in the NWB Tool. It is also a place for users of the NWB Tool, the Cyberinfrastructure Shell, or any other CIShell based program to get, upload, and request algorithms & datasets to be used in the tool. This site is a sounding board to be used by the community to work together and create a tool which will meet their needs and the needs of the scientific community at large.

The aims of clinical and translational research are to achieve a better understanding of the pathogenesis of human disease in order to develop effective diagnostic, therapeutic and prevention strategies. Biomedical informatics can play an important role in supporting this research by facilitating the management, integration, analysis and exchange of data derived from and related to the research problems being studied. A key aspect of this support is to bring clarity, rigor and formalism to the representation of 1. disease initiation, progression, pathogenesis, signs, symptoms, assessments, clinical and laboratory findings, disease diagnosis, treatment, treatment response and outcome, and 2. the interrelations between these distinct entities both in patient management and in clinical research, thus allowing the data to be more readily retrievable and shareable, and more able to serve in the support of algorithmic reasoning.

Mahout's goal is to build scalable, Apache licensed machine learning libraries. Initially, we are interested in building out the ten machine learning libraries detailed in http://www.cs.stanford.edu/people/ang//papers/nips06-mapreducemulticore.pdf using Hadoop. While these algorithms are our initial focus, we welcome contributions of other machine learning approaches.

Alchemy is a software package providing a series of algorithms for statistical relational learning and probabilistic logic inference, based on the Markov logic representation. Alchemy allows you to easily develop a wide range of AI applications, including: * Collective classification * Link prediction * Entity resolution * Social network modeling * Information extraction

BOSS (Build your Own Search Service) is Yahoo!'s open search web services platform. The goal of BOSS is simple: to foster innovation in the search industry. Developers, start-ups, and large Internet companies can use BOSS to build and launch web-scale search products that utilize the entire Yahoo! Search index. BOSS gives you access to Yahoo!'s investments in crawling and indexing, ranking and relevancy algorithms, and powerful infrastructure. By combining your unique assets and ideas with our search technology assets, BOSS is a platform for the next generation of search innovation, serving hundreds of millions of users across the Web.

Melita is an ontology-based text annotation tool. It implements a methodology with the intent to manage the whole annotation process for the users. It was noticed that several steps in the process, which till now are done manually can be easily automated and handled all by the system. The main competencies of Melita can be summarised into four groups, i.e. the Managing task, the Extraction, the Learning and the Information Tagging Autonomously. This is performed thanks to the use of a smart interface together with a powerfull Information extraction algorithm. Melita has now been replaced by AKTive Media , please click here to download AKTive Media, Melita is no longer used or available for download

In this article, we will explore the Map/Reduce approach to turn a sequential algorithm into parallel

In my previous article, I introduced the Map/Reduce model as a powerful model for parallelism. However, although Map/Reduce is simple, powerful, and provides a good opportunity to parallelize algorithm, it is based on a rigid procedural structure that requires injection of custom user code and therefore it is not easy to understand the big picture from a high level. You need to drill into the implementation code of the map and reduce functions in order to figure out what is going on.

OWL 2 extends the W3C OWL Web Ontology Language with a small but useful set of features that have been requested by users, for which effective reasoning algorithms are now available, and that OWL tool developers are willing to support. The new features include extra syntactic sugar, additional property and qualified cardinality constructors, extended datatype support, simple metamodeling, and extended annotations.

SCRIBO - Semi-automatic and Collaborative Retrieval of Information Based on Ontologies - aims at algorithms and collaborative free software for the automatic extraction of knowledge from texts and images, and for the semi-automatic annotation of digital documents.

Here, we present a computational model that learns structures of many different forms and that discovers which form is best for a given dataset. The model makes probabilistic inferences over a space of graph grammars representing trees, linear orders, multidimensional spaces, rings, dominance hierarchies, cliques, and other forms and successfully discovers the underlying structure of a variety of physical, biological, and social domains. Our approach brings structure learning methods closer to human abilities and may lead to a deeper computational understanding of cognitive development.